A Study for Semi-supervised Learning with Random Erasing

Yuuhi Okahana, Yusuke Gotoh

Research output: Chapter in Book/Report/Conference proceedingChapter


Due to the recent popularization of various data classified by computer, machine learning is attracting great attention. A common method of machine learning is supervised learning, which classifies data using a large number of class labeled training data called labeled data. To improve the processing performance of supervised learning, it is effective to use Random Erasing in data augmentation. However, since supervised learning requires much labeled data, the cost of manually adding label information to an unclassified training case (unlabeled data) is very high. In this paper, we propose a method for achieving high classification accuracy using Random Erasing for semi-supervised learning using few labeled data and unlabeled data. In our evaluation, we confirm the availability of the proposed method compared with conventional methods.

Original languageEnglish
Title of host publicationLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
Publication statusPublished - 2020

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Media Technology
  • Computer Science Applications


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